Paper Info
Title
Topic Modeling Based Sentiment Analysis On Social Media For Stock Market Prediction
Abstract
The goal of this research is to build a model to predict stock price movement using sentiments on social media. A new feature which captures topics and their sentiments simultaneously is introduced in the prediction model. In addition, a new topic model TSLDA is proposed to obtain this feature. Our method outperformed a model using only historical prices by about 6.07% in accuracy. Furthermore, when comparing to other sentiment analysis methods, the accuracy of our method was also better than LDA and JST based methods by 6.43% and 6.07%. The results show that incorporation of the sentiment information from social media can help to improve the stock prediction.
Year
Venue
Field
2015
PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1
Data mining,Stock price,Social media,Sentiment analysis,Computer science,Stock prediction,Artificial intelligence,Natural language processing,Topic model,Stock market prediction,Machine learning
DocType
Volume
Citations 
Conference
P15-1
30
PageRank 
References 
Authors
0.89
15
2
Name
Order
Citations
PageRank
Thien Hai Nguyen11114.50
Kiyoaki Shirai218218.08